Neurons represent the typical building block in living information processing systems, such as humans. In one model of a neuron, the neuron typically receives input pulses from one or more input neurons, and the neuron then generates output pulses based on the input pulses. The rate of the output pulses of a neuron represents the “state” of the neuron, and the state of the neuron varies depending on the input. As a result, as the inputs to the neuron change, the rate of output pulses also changes.
Computer systems have often been used in attempts to imitate the decision-making processes and behaviors of living information processing systems. Traditional computer systems often have difficulty imitating the behavior of living information processing systems because the computer systems and living information processing systems use different mechanisms in processing information. Living information processing systems typically excel at solving problems that are similar to previously-encountered problems. In contrast, traditional computer systems typically excel in performing exact sequences of steps on exact data or in performing predefined tasks in specific, limited circumstances. This often renders these computer systems unsuitable for imitating the complex and variable behavior of living information processing systems.